Dive into GANs, VAEs & Generative AI: Your First Concrete Step Today
GANs VAEs Generative AI: The Essentials in One Article — Real Code, Diagrams and Concrete Steps, Excerpts from a 43-Lesson Course.
The best way to learn GANs VAEs Generative AI is by doing. This article gives you a head start with practical excerpts from a 43-lesson course — enough to get your first results today.
- Introduction and Installation
- Fundamentals of Generative Models
- Autoencoders and VAEs
- GANs Fundamentals
- DCGAN and Conditional GAN
DDPM: Step-by-Step Implementation
Learning Objectives
- Define a noise schedule (beta)
- Add noise to an image in one step using the closed-form formula
- Understand the role of the U-Net as a noise predictor
- Write the DDPM training loop
- Implement the sampling loop
The noise schedule
We define a sequence of beta coefficients that control the amount of noise added at each step, from t=0 (little) to t=T (a lot). We derive cumulative quantities that allow direct noising.
Reparametrization trick and KL divergence
Learning Objectives
- Understand why sampling blocks the gradient
- Formulate the reparametrization trick
- Implement it in PyTorch
- Compute the KL divergence between two Gaussians
- Assemble the full VAE loss
The problem: we do not backpropagate through randomness
To train a VAE, we must sample z from N(mu, sigma). But the “draw at random” operation has no gradient: we cannot compute how a change in mu influences a random draw. Without a gradient, no learning via gradient descent is possible. This is the central blockage the trick circumvents.
z = sample(N(mu, sigma)) breaks the computation graph. The gradient cannot flow back to the encoder. The model would never learn to produce good mu and sigma.The solution: move randomness out of the way
The trick consists of rewriting the random draw so that the random part is independent of the learned parameters. We draw noise epsilon from a fixed standard Gaussian, then construct z with a deterministic operation.
With trick
Randomness is set aside (epsilon). The mu, sigma path remains differentiable.
PyTorch Implementation
Install Python, PyTorch and diffusers
Learning Objectives
- Create an isolated virtual environment
- Install PyTorch with or without CUDA GPU
- Verify that the GPU is detected
- Install diffusers, transformers and accelerate
- Know how to switch to Google Colab if no local GPU is available
Why an isolated environment?
Generative AI relies on libraries whose versions are extremely sensitive. A bad combination of PyTorch and CUDA versions can make the GPU invisible. To avoid polluting your system Python, we create a virtual environment dedicated to the course.
| Hardware | Command |
|---|---|
| NVIDIA GPU (CUDA 12.x) | pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 |
| CPU only | pip install torch torchvision |
| Mac Apple Silicon (MPS) | pip install torch torchvision |
Verify GPU detection
First reflex after any installation: check that PyTorch sees your accelerator. This small script will tell you everything.
Google Colab
No-install solution. PyTorch and CUDA are already present. Simply pip install diffusers and enable the GPU runtime.
This article covers the most useful excerpts — the complete GANs VAEs Generative AI course (11 chapters, 43 lessons, corrected exercises and final project) takes you all the way.
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